Hypergraph database for AI/ML that enables multi-hop reasoning across complex relationships. Store facts, entities, and episodes with native vector search.
Everything you need to build intelligent systems that understand context and relationships.
Connect any number of nodes in a single edge. Model complex N-ary relationships that traditional graphs cannot express.
Traverse relationships across multiple hops to discover hidden connections. Enable reasoning chains for RAG pipelines.
Combine vector similarity with graph traversal. Find semantically related knowledge within relationship context.
Built-in support for facts, entities, episodes, procedures, preferences, and goals. Structure knowledge the way humans think.
Track when facts were true and when they were recorded. Query knowledge as it existed at any point in time.
Store and query vector embeddings directly on nodes and edges. Powered by efficient cosine similarity search.
Simple, usage-based pricing. Start free, scale as you grow. No upfront costs.
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